Literature DB >> 12645997

Which concentration of the inhibitor should be used to predict in vivo drug interactions from in vitro data?

Kiyomi Ito1, Koji Chiba, Masato Horikawa, Michi Ishigami, Naomi Mizuno, Jun Aoki, Yasumasa Gotoh, Takafumi Iwatsubo, Shin-ichi Kanamitsu, Motohiro Kato, Iichiro Kawahara, Kayoko Niinuma, Akiko Nishino, Norihito Sato, Yuko Tsukamoto, Kaoru Ueda, Tomoo Itoh, Yuichi Sugiyama.   

Abstract

When the metabolism of a drug is competitively or noncompetitively inhibited by another drug, the degree of in vivo interaction can be evaluated from the [I]u/Ki ratio, where [I]u is the unbound concentration around the enzyme and Ki is the inhibition constant of the inhibitor. In the present study, we evaluated the metabolic inhibition potential of drugs known to be inhibitors or substrates of cytochrome P450 by estimating their [I]u/Ki ratio using literature data. The maximum concentration of the inhibitor in the circulating blood ([I]max), its maximum unbound concentration in the circulating blood ([I]max,u), and its maximum unbound concentration at the inlet to the liver ([I]in,max,u) were used as [I]u, and the results were compared with each other. In order to calculate the [I]u/Ki ratios, the pharmacokinetic parameters of each drug were obtained from the literature, together with their reported Ki values determined in in vitro studies using human liver microsomes. For most of the drugs with a calculated [I]in,max,u/Ki ratio less than 0.25, which applied to about half of the drugs investigated, no in vivo interactions had been reported or "no interaction" was reported in clinical studies. In contrast, the [I]max,u/Ki and [I]max/Ki ratio was calculated to be less than 0.25 for about 90% and 65% of the drugs, respectively, and more than a 1.25-fold increase was reported in the area under the concentration-time curve of the co-administered drug for about 30% of such drugs. These findings indicate that the possibility of underestimation of in vivo interactions (possibility of false-negative prediction) is greater when [I]max,u or [I]max values are used compared with using [I]in,max,u values.

Entities:  

Mesh:

Substances:

Year:  2002        PMID: 12645997      PMCID: PMC2751314          DOI: 10.1208/ps040425

Source DB:  PubMed          Journal:  AAPS PharmSci        ISSN: 1522-1059


  52 in total

1.  Diazepam and methadone blood levels following concurrent administration of diazepam and methadone.

Authors:  K L Preston; R R Griffiths; E J Cone; W D Darwin; C W Gorodetzky
Journal:  Drug Alcohol Depend       Date:  1986-10       Impact factor: 4.492

2.  Validation of the tolbutamide metabolic ratio for population screening with use of sulfaphenazole to produce model phenotypic poor metabolizers.

Authors:  M E Veronese; J O Miners; D Randles; D Gregov; D J Birkett
Journal:  Clin Pharmacol Ther       Date:  1990-03       Impact factor: 6.875

3.  Dapsone, trimethoprim, and sulfamethoxazole plasma levels during treatment of Pneumocystis pneumonia in patients with the acquired immunodeficiency syndrome (AIDS). Evidence of drug interactions.

Authors:  B L Lee; I Medina; N L Benowitz; P Jacob; C B Wofsy; J Mills
Journal:  Ann Intern Med       Date:  1989-04-15       Impact factor: 25.391

4.  Mexiletine and caffeine elimination.

Authors:  R Joeres; E Richter
Journal:  N Engl J Med       Date:  1987-07-09       Impact factor: 91.245

5.  High dose methylprednisolone increases plasma cyclosporin levels in renal transplant recipients.

Authors:  G Klintmalm; J Säwe
Journal:  Lancet       Date:  1984-03-31       Impact factor: 79.321

6.  Interaction of metoprolol, propranolol and atenolol with concurrent administration of cimetidine.

Authors:  W Kirch; H Spahn; H Köhler; E E Ohnhaus; E Mutschler
Journal:  Klin Wochenschr       Date:  1982-11-15

7.  Confirmation of the interaction between cyclosporine and the calcium channel blocker nicardipine in renal transplant patients.

Authors:  M Cantarovich; C Hiesse; F Lockiec; B Charpentier; D Fries
Journal:  Clin Nephrol       Date:  1987-10       Impact factor: 0.975

8.  The warfarin-sulfinpyrazone interaction: stereochemical considerations.

Authors:  S Toon; L K Low; M Gibaldi; W F Trager; R A O'Reilly; C H Motley; D A Goulart
Journal:  Clin Pharmacol Ther       Date:  1986-01       Impact factor: 6.875

9.  Interaction between propranolol and propafenone in healthy volunteers.

Authors:  P R Kowey; E B Kirsten; C H Fu; W D Mason
Journal:  J Clin Pharmacol       Date:  1989-06       Impact factor: 3.126

10.  Omeprazole inhibits oxidative drug metabolism. Studies with diazepam and phenytoin in vivo and 7-ethoxycoumarin in vitro.

Authors:  R Gugler; J C Jensen
Journal:  Gastroenterology       Date:  1985-12       Impact factor: 22.682

View more
  18 in total

Review 1.  Database analyses for the prediction of in vivo drug-drug interactions from in vitro data.

Authors:  Kiyomi Ito; Hayley S Brown; J Brian Houston
Journal:  Br J Clin Pharmacol       Date:  2004-04       Impact factor: 4.335

2.  Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models.

Authors:  Eleanor J Guest; Karen Rowland-Yeo; Amin Rostami-Hodjegan; Geoffrey T Tucker; J Brian Houston; Aleksandra Galetin
Journal:  Br J Clin Pharmacol       Date:  2011-01       Impact factor: 4.335

3.  To Apply Microdosing or Not? Recommendations to Single Out Compounds with Non-Linear Pharmacokinetics.

Authors:  Sieto Bosgra; Maria L H Vlaming; Wouter H J Vaes
Journal:  Clin Pharmacokinet       Date:  2016-01       Impact factor: 6.447

Review 4.  Pharmacokinetic drug interactions involving 17alpha-ethinylestradiol: a new look at an old drug.

Authors:  Hongjian Zhang; Donghui Cui; Bonnie Wang; Yong-Hae Han; Praveen Balimane; Zheng Yang; Michael Sinz; A David Rodrigues
Journal:  Clin Pharmacokinet       Date:  2007       Impact factor: 6.447

Review 5.  Predicting drug-drug interactions: an FDA perspective.

Authors:  Lei Zhang; Yuanchao Derek Zhang; Ping Zhao; Shiew-Mei Huang
Journal:  AAPS J       Date:  2009-05-06       Impact factor: 4.009

6.  The quantitative prediction of CYP-mediated drug interaction by physiologically based pharmacokinetic modeling.

Authors:  Motohiro Kato; Yoshihisa Shitara; Hitoshi Sato; Kunihiro Yoshisue; Masaru Hirano; Toshihiko Ikeda; Yuichi Sugiyama
Journal:  Pharm Res       Date:  2008-05-16       Impact factor: 4.200

7.  Potential impact of cytochrome P450 3A5 in human liver on drug interactions with triazoles.

Authors:  Hiroshi Yamazaki; Minako Nakamoto; Makiko Shimizu; Norie Murayama; Toshiro Niwa
Journal:  Br J Clin Pharmacol       Date:  2010-06       Impact factor: 4.335

8.  Prediction of in vivo drug-drug interactions from in vitro data : factors affecting prototypic drug-drug interactions involving CYP2C9, CYP2D6 and CYP3A4.

Authors:  Hayley S Brown; Aleksandra Galetin; David Hallifax; J Brian Houston
Journal:  Clin Pharmacokinet       Date:  2006       Impact factor: 6.447

9.  A new probabilistic rule for drug-dug interaction prediction.

Authors:  Jihao Zhou; Zhaohui Qin; Sara K Quinney; Seongho Kim; Zhiping Wang; Menggang Yu; Jenny Y Chien; Aroonrut Lucksiri; Stephen D Hall; Lang Li
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-01-21       Impact factor: 2.745

Review 10.  Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME.

Authors:  Patricio Godoy; Nicola J Hewitt; Ute Albrecht; Melvin E Andersen; Nariman Ansari; Sudin Bhattacharya; Johannes Georg Bode; Jennifer Bolleyn; Christoph Borner; Jan Böttger; Albert Braeuning; Robert A Budinsky; Britta Burkhardt; Neil R Cameron; Giovanni Camussi; Chong-Su Cho; Yun-Jaie Choi; J Craig Rowlands; Uta Dahmen; Georg Damm; Olaf Dirsch; María Teresa Donato; Jian Dong; Steven Dooley; Dirk Drasdo; Rowena Eakins; Karine Sá Ferreira; Valentina Fonsato; Joanna Fraczek; Rolf Gebhardt; Andrew Gibson; Matthias Glanemann; Chris E P Goldring; María José Gómez-Lechón; Geny M M Groothuis; Lena Gustavsson; Christelle Guyot; David Hallifax; Seddik Hammad; Adam Hayward; Dieter Häussinger; Claus Hellerbrand; Philip Hewitt; Stefan Hoehme; Hermann-Georg Holzhütter; J Brian Houston; Jens Hrach; Kiyomi Ito; Hartmut Jaeschke; Verena Keitel; Jens M Kelm; B Kevin Park; Claus Kordes; Gerd A Kullak-Ublick; Edward L LeCluyse; Peng Lu; Jennifer Luebke-Wheeler; Anna Lutz; Daniel J Maltman; Madlen Matz-Soja; Patrick McMullen; Irmgard Merfort; Simon Messner; Christoph Meyer; Jessica Mwinyi; Dean J Naisbitt; Andreas K Nussler; Peter Olinga; Francesco Pampaloni; Jingbo Pi; Linda Pluta; Stefan A Przyborski; Anup Ramachandran; Vera Rogiers; Cliff Rowe; Celine Schelcher; Kathrin Schmich; Michael Schwarz; Bijay Singh; Ernst H K Stelzer; Bruno Stieger; Regina Stöber; Yuichi Sugiyama; Ciro Tetta; Wolfgang E Thasler; Tamara Vanhaecke; Mathieu Vinken; Thomas S Weiss; Agata Widera; Courtney G Woods; Jinghai James Xu; Kathy M Yarborough; Jan G Hengstler
Journal:  Arch Toxicol       Date:  2013-08-23       Impact factor: 5.153

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.